A Preliminary Study of the Energy Impact of Software in Raspberry Pi devices

Kamar, Kesrouani,; Kanso, Houssam; Noureddine, Adel

doi:10.1109/wetice49692.2020.00052

Cited by 11 publications

(4 citation statements)

References 5 publications

(6 reference statements)

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“…Edge machine learning techniques and advances in tinyML computer vision applications provide promising ways to achieve this goal [8,9]. Studies focusing on Raspberry Pi applications underscore the importance of more efficient and effective code for these devices [10][11][12]. Manual optimization methods are often more effective than automated compiler optimization, emphasizing the need for customized methods [13].…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

Islam,

Sayeedi,

Deepti

et al. 2024

Preprint

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In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

Islam,

Sayeedi,

Deepti

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Edge machine learning techniques and advances in tinyML computer vision applications provide promising ways to achieve this goal [9,10]. Studies focusing on Raspberry Pi applications underscore the importance of more efficient and effective code for these devices [11][12][13]. Manual optimization methods are often more effective than automated compiler optimization, emphasizing the need for customized methods [14].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

Sayeedi,

Deepti,

Osmani

et al. 2024

Applied Sciences

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In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML tasks: image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

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“…The study investigated the granularity at which RAPL measurements can be made as well as the practical challenges that can be encountered on today's complex CPUs. Kesrouani et al (2020) developed a model to estimate how much energy is consumed by a device while accounting for CPU usage. The model typically had a 1.25 percent error rate.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…In a previous work, García-Martín et al (2019) asserts that there is an insufficient knowledge in the present methods to estimate energy consumption in machine learning frameworks. Subsequently, Kesrouani et al (2020) develop a software-based model that determines energy consumption of computing devices. Further, Berthou et al (2020) propose a method for estimating the power consumption of peripheral devices for a low-power embedded micro-controllers.…”

Section: Introductionmentioning

confidence: 99%

Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection

Baidoo

Yaokumah

Owusu

2023

International Journal of Information Technologies and Systems Approach

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Estimating the energy and memory consumption of machine learning(ML) models for intrusion detection ensures efficient allocation of system resources. This study investigates the impact of supervised ML algorithms on the energy and memory consumption of intrusion detection systems. Experiments are conducted with seven ML algorithms and a proposed ensemble model, utilizing two intrusion detection datasets. Pearson correlation coefficient(PCC) and Spearman correlation coefficient are employed for the selection of optimum features. Regarding energy consumption, the findings reveal that the PCC with the UNSW-NB15 dataset uses the least amount of DRAM and CPU power. For ML methods, SVM utilizes the highest energy for both feature selection methods and datasets. Concerning memory consumption, the results show that decision tree uses the most current memory with PCC on the UNSW-NB15. The proposed ensemble model demonstrates the highest performance. These findings offer practical guidelines to ML experts when choosing the optimum model with the most efficient utilization of energy and memory.

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A Preliminary Study of the Energy Impact of Software in Raspberry Pi devices

Cited by 11 publications

References 5 publications

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices

Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection

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